Economic & Financial
Data Analysis II
This subject consists of two parts.
• Part I (Lecturer: Tin Nguyen)
Basic elements of probability & statistics.
Fundamentals of regression analysis.
Part II (Lecturer: Eran Binenbaum)
Topics to be given later.
EFDA II Main & Other
Textbooks
• Main textbook (for both parts)
– Analysis of Economic Data
by (Wiley)
• Other textbooks
– Introduction to Econometrics
by Stock & Watson (Ch. 1-5 & 12)
– Essential of Econometrics
by Gujarati (Ch. 1-7)
– Using Econometrics
by Studenmund
(Ch. 1-6 & 11)
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Lectures
• Lectures
– focus on the key topics
– explain difficult topics from textbook
– show worked examples
– provide extra materials not in textbook.
• Lecture Notes
– Detailed notes.
– Copies of lectures are downloadable from
MyUni
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Assessment
• Exam 3 hours
70%
• Tutorial (both parts)
10%
• Tests (Parts I & II)
20%
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Tutorials (10%)
• One mark will be given for each
tutorial attended and satisfactorily
participated, up to a maximum of 10
marks (i.e. each student can receive a
full 10 marks for tutorials with one
absent). One mark will also be given
for each absence if a justifiable reason
can be given with adequate evidence,
e.g. a medical certificate.
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Objective of Part 1
• The objective of part 1 of this subject is
to help you to acquire an elementary
background in probability theory and
statistics necessary for the proper
understanding of econometric tests and
problems and fundamentals of
regression analysis.
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Real-world Questions
• Some real world questions may appear to require
a YES or NO answer, e.g.
– Are women being discriminated against in pay?
– Are people with a university degree more likely
to be employed and earn more than those
without a degree?
• However, on closer scrutiny, we often want
numerical answers to such questions, e.g.
– How much more likely a person with a degree
will find employment than a person without such
a degree, other things being equal.
– How much a woman earns less than a man,
other things being equal (i.e. all other individual
EFDA II, Semester 2, 2004
characteristics
– age, education, experience,..,7 -
Choice of Topics
• Here, the focus will be on estimation
procedures and tests that are commonly
used in practice. Fore example, apart from
ordinary least squares (OLS) regression, we
may have to deal with:
– Instrumental variable regression, which
arises when the residual is expected to be
correlated with a stochastic regressor. A
valid instrument is assumed to be
exogenous (uncorrelated with the residual)
and correlated reasonably well with the
variable it is used to represent.
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Distinct Features of EFDA II
• To ensure that the tools learning in EFDA II are
of practical value to real life research, the
following three distinct features are adopted.
– Large sample approach.
• This involves the use of large-sample
approximations to sampling distributions for
hypothesis testing and confidence intervals.
• Note that small sample t-distribution or Fdistribution requires the assumption that the
residual has a normal distribution, an assumption
which does not often hold in practice.
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Three Distinct Features of EFDA II
– Stochastic regressors.
Regressors are assumed to be random
rather than to be fixed in repeated
sampling.
– Heteroscedasticity.
Heteroscedastic errors are generally
assumed.
Heteroscedasticity-robust standard errors will
be used to eliminate worries about whether
EFDA II, Semester 2, 2004 is present or not (i.e. there
heteroscedasticity
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Contemporary Choice of Topics
It is useful to discuss briefly here some
important topics which are covered in
the textbook but are not included in
EFDA II.
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Instrumental variables regression
• Instrumental variables regression is presented
as a general method for handling correlation
between the error term and a regressor, (which
can arise for many reasons, including
simultaneous causality).
• The two assumptions for a valid instrument are:
exogeneity and relevance.
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Program Evaluation
• An increasing number of econometric studies
analyse either randomised controlled
experiments or quasi-experiments, also
known as natural experiments.
• This research strategy can be presented as an
alternative approach to the problems of
• omitted variables &
• simultaneous causality
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Forecasting
• Forecasting topic considers univariate
(autoregressive) and multivariate forecasts
using time series regression, not large
simultaneous equation structural models.
• This topic also features a practically oriented
treatment of stochastic trends
• unit root tests,
• tests for structural breaks &
• pseudo out-of-sample forecasting
all in the context of developing stable and
reliable time series fore casting models.
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Skilled Producers & Sophisticated
Consumers of Empirical Results
• It is hoped that students using this book will
become skilled producers and sophisticated
critical users of empirical results.
• To do so, they must learn not only how to
use the tools of regression analysis, but also
how to assess the validity of empirical
analyses presented to them.
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Threats to Internal & External Validity
After learning the main tools of regression
analysis, the threats to internal and external
validity of an empirical study should be
considered:
 
 
data problems & issues of generalizing findings to
other settings
main threats to regression analysis, including:
o
o
o
o
omitted variables,
functional form misspecification,
errors-in-variables, and
simultaneity.
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TOPIC 1: Economic Questions and Data
• Many important real-world questions demand specific
numerical answers, e.g.
Do smaller elementary school class sizes produce higher
test scores?
What is the price elasticity of the demand for cigarettes?
What is the private returns to an additional year of
education?
• The aim is to show you how you can learn from data
but at the same time be self-critical and aware of the
limitations of empirical analyses.
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A special message for those intended to
carry out research for a dissertation for:
B.Ec. (Honours) in Economics
Master of Applied Economics
Master of Economics
Ph.D. in Economics
Choose a thesis topic in which there is sufficient
scope and data for applying the analytical and
econometric or statistical tools you have learned
from your econometric and economic theory
subjects at levels II and III or higher, e.g. EFDAII,
Applied Econometrics III, Micro II, Macro II,
Economic Theory III, etc.
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Economic Questions
1.1 Economic Questions
• Many decisions in economics, business, and
government hinge on understanding relationships
among variables in the world around us. These
decisions require quantitative answers to
quantitative questions. The following four of these
questions concern education policy, racial bias in
mortgage lending, cigarette consumption, and
macroeconomic forecasting.
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Question 1: Does Reducing Class Size
Improve Elementary School Education?
 One prominent proposal for improving basic
learning is to reduce class sizes at elementary
schools.
 With fewer students in the classroom, the
argument goes, each student gets more of the
teacher's attention, there are fewer class
disruptions, learning is enhanced, and grades
improve.
 For practical reasons, we also have to ask the
following questions.
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But what is the effect on elementary school
education of reducing class size?
 Reducing class size costs money (It
requires hiring more teachers and, if the
school is already at capacity, building more
classrooms.)
 To weigh costs and benefits, however, the
decision maker must have a precise
quantitative understanding of the likely
benefits.
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Is the beneficial effect on basic learning of
smaller classes large or small?
Is it possible that smaller class size actually
has no effect on basic learning?
• Common sense cannot provide a quantitative
answer to the question of what exactly is the
effect on basic learning of reducing class size.
• To provide such an answer, we must
examine empirical evidence, that is, evidence
based on data-relating class size to basic
learning in elementary schools.
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The Benefits-Costs of Class Size: An Example
• As an example, let us examine the relationship between
class size and basic learning using data gathered from
420 California school districts in 1998.
• In the California data, students in districts with small
class sizes tend to perform better on standardised tests
than students in districts with larger classes.
• While this fact is consistent with the idea that smaller
classes produce better test scores, it might simply
reflect many other advantages that students in districts
with small classes have over their counterparts in
districts with large classes.
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The Benefits-Costs of Class Size: An Example (cont.)
• For example, districts with small class sizes tend to
have wealthier residents than districts with large
classes, so students, in small-class districts could have
more opportunities for learning outside the classroom.
It could be these extra learning opportunities that lead
to higher test scores, not smaller class sizes.
• We can use multiple regression analysis to isolate the
effect of changes in class size from changes in other
factors, such as the economic background of the
students.
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Question 2: Is There Racial Discrimination
in the Market for Home Loans?
• By law, U.S. lending institutions cannot take race into
account when deciding to grant or deny a request for a
mortgage: applicants who are identical in all ways but their
race should be equally likely to have their mortgage
applications approved. In theory, then, there should be
no racial bias in mortgage lending.
• However, researchers at the Federal Reserve Bank of
Boston found (using data from the early 1990s) that 28%
of black applicants are denied mortgages, while only 9% of
white applicants are denied. Do these data indicate that,
in practice, there is racial bias in mortgage lending? If
so, how large is it?
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Is There Racial Discrimination in the
Market for Home Loans?
• The fact that more black than white applicants are denied
in the Boston Fed data does not by itself provide evidence
of discrimination by mortgage lenders, because the black
and white applicants differ in many ways other than their
race.
• Before concluding that there is bias in the mortgage
market, these data must be examined more closely to see if
there is a difference in the probability of being denied for
otherwise identical applicants and, if so, whether this
difference is large or small.
• To do this requires econometric methods that make it
possible to quantify the effect of race on chance of
obtaining a mortgage, holding constant other applicant
characteristics, notably their ability to repay the loan.
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Question 3: How Much Do Cigarette Taxes
Reduce Smoking?
• Cigarette smoking is a major public health concern
worldwide. Many of the costs of smoking, such as the
medical expenses of caring for those made sick by smoking
and the less quantifiable costs to non-smokers who prefer not
to breathe second-hand cigarette smoke, are borne by other
members of society.
• Because these costs are borne by people other than the
smokers - there is a role for government intervention in
reducing cigarette consumption. One of the most flexible
tools for cutting consumption is to increase taxes on
cigarettes.
Basic economics says that if cigarette prices go
up, consumption will go down. But by how
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much?
If the sales price goes up by 1%, by what
percentage will the quantity of cigarettes sold
decrease?
• The percentage change in the quantity demanded
resulting from a 1% increase in price is the price
elasticity of demand.
• If we want to reduce smoking by a certain amount,
say 20%, by raising taxes, then we need to know
the price elasticity to calculate the price increase
necessary to achieve this reduction in
consumption.
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But what is the price elasticity of demand for
cigarettes?
• Although economic theory provides us with the
concepts that help us answer this question, it does not
tell us the numerical value of the price elasticity of
demand. To learn the elasticity we must examine
empirical evidence about the behaviour of smokers
and potential smokers; in other words, we need to
analyse data on cigarette consumption and prices.
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Cigarette Taxes and Sales
• The data we examine are cigarette sales, prices, taxes,
and personal income for U.S. states in the 1980s and
1990s.
• In these data, states with low taxes, and thus low
cigarette prices, have high smoking rates, and states
with high prices have low smoking rates.
• However, the analysis of these data is complicated
because causality runs both ways: low taxes lead to
high demand, but if there are many smokers in the state
then local politicians might try to keep cigarette taxes
low to satisfy their smoking constituents.
• Methods for handling this "simultaneous causality",
not covered in this course, should be used to estimate
the priceEFDA
elasticity
of cigarette demand.
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Question 4: What Will the Rate of Inflation
Be Next Year?
It seems that people always want a sneak
preview of the future.
What will sales be next year at a firm
considering investing in new equipment?
Will the stock market go up next month and, if
so, by how much?
Will city tax receipts next year cover planned
expenditures on city services?
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Inflation Forecasting
• One aspect of the future in which
macroeconomists and financial economists are
particularly interested is the rate of overall price
inflation during the next year.
• Professional economists (who require on precise
numerical forecasts) use econometric models to
make those forecasts.
• A forecaster's job is to predict the future using the
past, and econometricians do this by using
economic theory and statistical techniques to
quantify relationships in historical data.
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Inflation Forecasting: Model
• The data we use to forecast inflation are the
rates of inflation and unemployment in the
United States.
• An important empirical relationship in
macroeconomic data is the "Phillips curve,"
in which a currently low value of the
unemployment rate is associated with an
increase in the rate of inflation over the
next year.
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Quantitative Questions & Quantitative Answers
• Each of the above four questions requires a
numerical answer.
• Economic theory provides clues about that
answer-cigarette consumption ought to go
down when the price goes up-but the actual
value of the number must be learned
empirically, that is, by analysing data.
• Because we use data to answer quantitative
questions, our answers always have some
uncertainty: a different set of data would
produce a different numerical answer.
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Quantitative Questions & Quantitative
Answers
• Therefore, the conceptual framework for the
analysis needs to provide both a numerical
answer to the question and a measure of how
precise the answer is.
• The conceptual framework used in this book
is the multiple regression model, the
mainstay of econometrics.
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The Role of Econometric Model
• Economic model provides a mathematical way to
quantify how a change in one variable affects
another variable, holding other things constant, e.g.
– What effect does a change in class size
have on test scores, holding constant
student characteristics (e.g. family
income) that a school district
administrator cannot control?
– What effect does your race have on your
chances of having a mortgage application
granted, holding constant other factors such as
your
ability to repay the loan?
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The Role of Econometric Model
– What effect does a 1% increase in the
price of cigarettes have on cigarette
consumption, holding constant the
income of smokers and potential
smokers?
• The multiple regression model and its
extensions provide a framework for
answering these questions using data and for
quantifying the uncertainty associated with
those answers.
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